Search Results for author:
Found 7 papers, 2 papers with code
Reproducibility analysis of automated deep learning based localisation of mandibular canals on a temporal CBCT dataset
The RM of the Likert scoring was 0. 923 for the radiologist and 0. 877 for the DLS.
Comparison of Deep Learning Segmentation and Multigrader-annotated Mandibular Canals of Multicenter CBCT scans
Deep learning approach has been demonstrated to automatically segment the bilateral mandibular canals from CBCT scans, yet systematic studies of its clinical and technical validation are scarce.
Uncertainty-aware deep learning methods for robust diabetic retinopathy classification
Automatic classification of diabetic retinopathy from retinal images has been widely studied using deep neural networks with impressive results.
Knowledge mining of unstructured information: application to cyber-domain
In this article we present and implement a novel knowledge graph and knowledge mining framework for extracting the relevant information from free-form text about incidents in the cyberdomain.
Deep Learning Fundus Image Analysis for Diabetic Retinopathy and Macular Edema Grading
Diabetes is a globally prevalent disease that can cause visible microvascular complications such as diabetic retinopathy and macular edema in the human eye retina, the images of which are today used for manual disease screening.
Sampling networks by nodal attributes
Assuming that the nodal attributes are independently drawn from an arbitrary distribution $\rho(h)$ and that the sampling probability $r(h_i , h_j)$ for a link $ij$ of nodal attributes $h_i$ and $h_j$ is also arbitrary, we are able to derive exact analytic expressions of the sampled network for such network characteristics as the degree distribution, degree correlation, and clustering spectrum.
Physics and Society Social and Information Networks
What does Big Data tell? Sampling the social network by communication channels
For example, while it is expected that the degree distribution of the whole social network has a maximum at a value larger than one, we get a monotonously decreasing distribution as observed in empirical studies of single channel data.
Physics and Society Social and Information Networks
